Efficient Touching Text Line Segmentation in Tamil Script Using Horizontal Projection

Kathirvalavakumar, T.; Selvi, M. Karthigai

doi:10.1007/978-3-319-03844-5_29

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Words are separated successfully from all the thousand documents. 14, ( 20), (31), (15) , (20), (21) and (13) with CT(27) is shown in Fig. 12 (width of the characters within the parentheses specifies the width of the character).…”

Section: Resultsmentioning

confidence: 99%

Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Karthigaiselvi¹,

Kathirvalavakumar²

2017

IJERA

View full text Add to dashboard Cite

Feature Extraction plays most crucial and important role in character recognition. The selection of stable and representative set of features is the main problem in pattern recognition. Because of font characteristics and style variation of machine printed Tamil characters, feature extraction remains a problem. Feature extraction involves reducing the amount of resources required to describe a set of data. In this paper, new method has been proposed to extract structural features from Machine printed Tamil characters using horizontal and vertical projections. Based on the structural properties of upper and lower modifiers, characters are divided into various categories and features are extracted accordingly. The extracted features from the real life degraded documents are classified to identify the characters. The system has been tested with printed Tamil characters and achieves 99.67% character recognition accuracy on average. Experimental results show that structure and category of the characters are identified by the proposed method for the regular characters of various sizes.

show abstract

Section: Resultsmentioning

confidence: 99%

Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Karthigaiselvi¹,

Kathirvalavakumar²

2017

IJERA

View full text Add to dashboard Cite

show abstract

“…It becomes difficult to estimate the exact position of a row which segments a line from the next line. To segment this kind of overlapped lines Projection Based Lines Segmentation (PBLS) [8] is used. There is a possibility to have two or more overlapped lines when the strip has projection value greater than the one-third of average projection values.…”

Section: Word Segmentationmentioning

confidence: 99%

Recognition of Words in Tamil Script Using Neural Network

Karthigaiselvi¹,

Kathirvalavakumar²

2017

IJERA

View full text Add to dashboard Cite

In this paper, word recognition using neural network is proposed. Recognition process is started with the partitioning of document image into lines, words, and characters and then capturing the local features of segmented characters. After classifying the characters, the word image is transferred into unique code based on character code. This code ideally describes any form of word including word with mixed styles and different sizes. Sequence of character codes of the word form input pattern and word code is a target value of the pattern. Neural network is used to train the patterns of the words. Trained network is tested with word patterns and is recognized or unrecognized based on the network error value. Experiments have been conducted with a local database to evaluate the performance of the word recognizing system and obtained good accuracy. This method can be applied for any language word recognition system as the training is based on only unique code of the characters and words belonging to the language.

show abstract

Efficient segmentation of printed tamil script into characters using projection and structure

Kathirvalavakumar

Mohan

2017

2017 Fourth International Conference on Image Information Processing (ICIIP)

View full text Add to dashboard Cite

Efficient Touching Text Line Segmentation in Tamil Script Using Horizontal Projection

Cited by 3 publications

References 11 publications

Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Recognition of Words in Tamil Script Using Neural Network

Efficient segmentation of printed tamil script into characters using projection and structure

Contact Info

Product

Resources

About